808 research outputs found
Named Entity Recognition in Twitter using Images and Text
Named Entity Recognition (NER) is an important subtask of information
extraction that seeks to locate and recognise named entities. Despite recent
achievements, we still face limitations with correctly detecting and
classifying entities, prominently in short and noisy text, such as Twitter. An
important negative aspect in most of NER approaches is the high dependency on
hand-crafted features and domain-specific knowledge, necessary to achieve
state-of-the-art results. Thus, devising models to deal with such
linguistically complex contexts is still challenging. In this paper, we propose
a novel multi-level architecture that does not rely on any specific linguistic
resource or encoded rule. Unlike traditional approaches, we use features
extracted from images and text to classify named entities. Experimental tests
against state-of-the-art NER for Twitter on the Ritter dataset present
competitive results (0.59 F-measure), indicating that this approach may lead
towards better NER models.Comment: The 3rd International Workshop on Natural Language Processing for
Informal Text (NLPIT 2017), 8 page
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